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We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: (i) the initial prior beliefs, (ii) the weights attached on priors, and (iii)...
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